most citedDARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

34 citations · 44 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY20248 cited

Unleashing OpenTitan's Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading

Maicol Ciani, Emanuele Parisi, Alberto Musa +7

The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical…

cs.AR20242 cited

A Gigabit, DMA-enhanced Open-Source Ethernet Controller for Mixed-Criticality Systems

Chaoqun Liang, Alessandro Ottaviano, Thomas Benz +4

The ongoing revolution in application domains targeting autonomous navigation, first and foremost automotive "zonalization", has increased the importance of certain off-chip commun…

cs.AR2023

AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

Thomas Benz, Alessandro Ottaviano, Robert Balas +4

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufacturing processes have led to the transition from federated to inte…

cs.AR2023

Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays

Mattia Sinigaglia, Luca Bertaccini, Luca Valente +5

Emerging applications in the IoT domain require ultra-low-power and high-performance end-nodes to deal with complex near-sensor-data analytics. Domains such as audio, radar, and St…

cs.AR202334 cited

DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

Angelo Garofalo, Yvan Tortorella, Matteo Perotti +5

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solut…